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<p>This study aimed to develop a deep learning–based method for automatic segmentation of the pharyngeal area (PA) and measurement of the pharyngeal contraction ratio (PCR) during deglutition using cine magnetic resonance imaging (MRI). The proposed algorithm combines PA region extraction by a 2D U-Net with automatic calculation of PA and PCR. Segmentation performance was evaluated using the Dice coefficient (DC), and the PCR measured by the model (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{PCR}_{model}\)</EquationSource> </InlineEquation>) was compared with that obtained manually (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:{PCR}_{label}\)</EquationSource> </InlineEquation>) using correlation and Bland–Altman analyses. Cine MRI data of 20 healthy adults (10 men, 10 women; age 22–29 years) were analyzed. The average DC in the test cases was 0.890 ± 0.025, and the PA of the model correlated well with the manual reference (<i>r</i> = 0.70–0.97). The mean <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\:{PCR}_{model}\)</EquationSource> </InlineEquation> was 0.105 ± 0.035, consistent with values reported in videofluoroscopic swallowing studies. These results demonstrate the technical feasibility of automatic PCR measurement from cine MRI using deep learning.</p>

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Automatic measurement of pharyngeal contraction ratio during deglutition using 2D cine MRI with deep learning: A pilot study

  • 期刊论文

  • 发表日期：

    2025年11月14日

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  • Masato Takahashi,
  • Naoka Miyamoto,
  • Norikazu Koori,
  • Masahiko Monma,
  • Yoshiyuki Ishimori,
  • Hiraku Fuse,
  • Shin Miyakawa,
  • Kenji Yasue,
  • Hiroki Nosaka,
  • Shinji Abe

摘要

This study aimed to develop a deep learning–based method for automatic segmentation of the pharyngeal area (PA) and measurement of the pharyngeal contraction ratio (PCR) during deglutition using cine magnetic resonance imaging (MRI). The proposed algorithm combines PA region extraction by a 2D U-Net with automatic calculation of PA and PCR. Segmentation performance was evaluated using the Dice coefficient (DC), and the PCR measured by the model ( \(\:{PCR}_{model}\) ) was compared with that obtained manually ( \(\:{PCR}_{label}\) ) using correlation and Bland–Altman analyses. Cine MRI data of 20 healthy adults (10 men, 10 women; age 22–29 years) were analyzed. The average DC in the test cases was 0.890 ± 0.025, and the PA of the model correlated well with the manual reference (r = 0.70–0.97). The mean \(\:{PCR}_{model}\) was 0.105 ± 0.035, consistent with values reported in videofluoroscopic swallowing studies. These results demonstrate the technical feasibility of automatic PCR measurement from cine MRI using deep learning.